JP2015184158A - Error estimation method, motion analysis method, error estimation device, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an error estimation method, an error estimation device, and a program that are capable of precisely estimating errors of an index showing the state of a moving body, and a motion analysis method capable of precisely analyzing motions of a user.SOLUTION: An error estimation method according to the present invention includes the steps of selecting one of first and second modes based on the detection results of a first sensor and estimating errors of an index showing the state of a moving body, the errors estimating step including the sub-steps of predicting the errors and correcting the predicted errors not using a second reference value based on the detection results of a second sensor but using a first reference value based on the detection results of the first sensor and a first error of the errors if the first mode has been selected, and correcting the predicted errors not using the first reference value but using the second reference value and the first error if the second mode has been selected.

Description

  The present invention relates to an error estimation method, a motion analysis method, an error estimation device, and a program.

  Inertial navigation that calculates the position and speed of a moving object using the detection result of an inertial sensor is widely known. If a person is assumed to be a moving body and an inertial sensor is attached to the torso, the posture (orientation angle) of the torso changes from moment to moment as the person moves. It cannot be specified accurately, and the calculation accuracy of the position and speed is lowered.

  When calculating the attitude angle from the detection result of the inertial sensor, the process of calculating the rotation amount from the detection result and integrating (rotating calculation) the previous attitude angle is repeated. However, since the detection result of the inertial sensor includes a bias error, it is necessary to accurately estimate the posture angle error due to the bias error and correct the posture angle. For example, Patent Document 1 describes a technique for estimating a measurement error of an inertial navigation device using a Kalman filter from measurement values of a radio navigation device and an inertial navigation device using GPS (Global Positioning System).

JP 2001-174275 A

  However, high estimation accuracy is not always ensured depending on the GPS measurement accuracy and measurement timing. Thus, it is conceivable to use a posture angle error estimation method using GPS and another posture angle error estimation method (for example, an error estimation method using regularity of movement of a moving object). However, when the posture angle error is estimated by using two different methods in combination, double estimation is performed and the estimation may not be performed correctly.

  The present invention has been made in view of the above problems, and according to some aspects of the present invention, an error estimation that can accurately estimate an error of an index representing the state of a moving object. It is possible to provide a method, an error estimation device and program, and a motion analysis method capable of accurately analyzing a user's motion.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following aspects or application examples.

[Application Example 1]
The error estimation method according to this application example selects one of the first mode and the second mode based on the detection result of the first sensor, and estimates an error of an index representing the state of the moving object. Estimating the error includes: predicting the error; and selecting the first mode without using a second reference value based on a detection result of a second sensor. The first reference value when the predicted error is corrected using the first reference value based on the detection result of one sensor and the first error among the errors, and when the second mode is selected. And correcting the predicted error using the second reference value and the first error.

  According to the error estimation method according to this application example, one of two different modes for correcting the error of the index representing the state of the moving object using different types of reference values and the same type of error is selected. To do. Since the correction in the first mode and the correction in the second mode do not overlap, the error can be estimated with high accuracy.

[Application Example 2]
In the error estimation method according to the application example, the second sensor may include at least one of an acceleration sensor and an angular velocity sensor.

  According to the error estimation method according to this application example, in the second mode, the error can be estimated using the reference value based on the detection result of the acceleration sensor or the angular velocity sensor.

[Application Example 3]
In the error estimation method according to the application example, the first sensor may be a sensor that receives a signal from a positioning satellite or a geomagnetic sensor.

  According to the error estimation method according to this application example, in the first mode, the error can be estimated using a reference value based on a detection result of a sensor that receives a signal from a positioning satellite or a geomagnetic sensor.

[Application Example 4]
In the error estimation method according to the application example, when the detection result of the first sensor is obtained, the first mode is selected, and when the detection result of the first sensor is not obtained, the second mode is selected. A mode may be selected.

  According to the error estimation method according to this application example, the first mode can be selected when the detection result of the first sensor is obtained.

[Application Example 5]
In the error estimation method according to the application example, when the accuracy of the detection result of the first sensor is equal to or higher than a reference value, the first mode is selected, and the accuracy of the detection result of the first sensor is less than the reference value. In some cases, the second mode may be selected.

  According to the error estimation method according to this application example, the second mode is selected when the accuracy of the reference value based on the detection result of the first sensor is less than the reference value, so that a decrease in error estimation accuracy is suppressed. Can do.

[Application Example 6]
In the error estimation method according to the application example, when the first mode is selected, a third reference value based on a detection result of the second sensor is different from the first error among the errors. And correcting the predicted error using two errors.

  According to the error estimation method according to this application example, in the first mode, the error estimation using the first reference value based on the detection result of the first sensor and the third reference value based on the detection result of the second sensor are used. Since error estimation can be performed, the accuracy of error estimation in the first mode can be further improved.

[Application Example 7]
The motion analysis method according to this application example includes estimating the error using any one of the error estimation methods described above, correcting the index using the estimated error, and correcting the corrected index. And analyzing the movement of the moving body.

  According to the motion analysis method according to this application example, the motion of the mobile object using the index accurately corrected using the error of the index representing the state of the mobile object estimated using the error estimation method according to the application example described above. Can be analyzed with high accuracy.

[Application Example 8]
The error estimation apparatus according to the application example estimates a mode selection unit that selects one of the first mode and the second mode based on the detection result of the first sensor, and an error of an index that represents the state of the moving body. An error estimation unit that predicts the error, and when the first mode is selected, without using a second reference value based on a detection result of the second sensor, When the predicted error is corrected using the first reference value based on the detection result of the first sensor and the first error among the errors, and the second mode is selected, the first The predicted error is corrected using the second reference value and the first error without using a reference value.

  According to the error estimation device according to this application example, one of two different modes for correcting the error of the index representing the state of the moving object using different types of reference values and the same type of error is selected. To do. Since the correction in the first mode and the correction in the second mode do not overlap, the error can be estimated with high accuracy.

[Application Example 9]
The program according to this application example selects one of the first mode and the second mode based on the detection result of the first sensor, estimates an error of an index representing the state of the moving object, To estimate the error is to predict the error and to select the first mode without using the second reference value based on the detection result of the second sensor. The first reference is obtained when the predicted error is corrected using the first reference value based on the detection result of the first sensor and the first error among the errors, and when the second mode is selected. Correcting the predicted error using the second reference value and the first error without using a value.

  According to the program according to this application example, one of two different modes for correcting the error of the index representing the state of the moving body using different types of reference values and the same type of error is selected. Since the correction in the first mode and the correction in the second mode do not overlap, the error can be estimated with high accuracy.

Explanatory drawing about the outline | summary of the exercise | movement analysis system of this embodiment. The functional block diagram which shows the structural example of a motion analysis apparatus and a display apparatus. The figure which shows the structural example of a sensing data table. The figure which shows the structural example of a GPS data table. The figure which shows the structural example of a calculation data table. The functional block diagram which shows the structural example of the process part of a motion analysis apparatus. Explanatory drawing about the posture at the time of a user's walk. Explanatory drawing about the yaw angle at the time of a user's walk. The figure which shows an example of the triaxial acceleration at the time of a user's walk. The flowchart figure which shows an example of the procedure of an exercise | movement analysis process. The flowchart figure which shows an example of the procedure of a walk detection process.

DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below do not unduly limit the contents of the present invention described in the claims. Also, not all of the configurations described below are essential constituent requirements of the present invention.

1. Motion analysis system 1-1. Overview of System FIG. 1 is a diagram for describing an overview of a motion analysis system 1 of the present embodiment. As shown in FIG. 1, the motion analysis system 1 according to the present embodiment includes a motion analysis device 2 and a display device 3. The motion analysis device 2 is attached to a trunk portion (for example, right waist or left waist) of a user (an example of a moving body). The motion analysis apparatus 2 has an inertial measurement unit (IMU) 10 and captures movements of the user's walking (including running) to detect speed, position, and posture angle (roll angle, pitch angle, yaw angle). ) Etc., and further, the motion of the user is analyzed to generate motion analysis information. In the present embodiment, the motion is performed so that one detection axis (hereinafter referred to as z-axis) of the inertial measurement unit (IMU) 10 substantially coincides with the gravitational acceleration direction (vertically downward) while the user is stationary. The analysis device 2 is attached. The motion analysis device 2 transmits the generated motion analysis information to the display device 3.

  The display device 3 is a wrist-type (wristwatch-type) portable information device, and is attached to a user's wrist or the like. However, the display device 3 may be a portable information device such as a head mounted display (HMD) or a smartphone. The user can instruct the start and stop of measurement by the motion analysis device 2 by operating the display device 3. The display device 3 transmits a command for instructing measurement start or measurement stop to the motion analysis device 2. When the motion analysis device 2 receives a measurement start command, the motion analysis device 2 starts measurement by the inertial measurement unit (IMU) 10, analyzes the user's motion based on the measurement result, and generates motion analysis information. The motion analysis device 2 transmits the generated motion analysis information to the display device 3, the display device 3 receives the motion analysis information, and presents the received motion analysis information to the user in various forms such as letters, figures, and sounds. . The user can recognize the motion analysis information via the display device 3.

  Data communication between the motion analysis device 2 and the display device 3 may be wireless communication or wired communication.

  In the present embodiment, a case where the motion analysis apparatus 2 estimates a user's walking speed and generates motion analysis information including a travel route and a travel time will be described in detail below as an example. This motion analysis system 1 can be similarly applied to the case of generating motion analysis information in motion involving movement other than walking.

1-2. Coordinate system The coordinate system required in the following description is defined.
・ E Frame (Earth Center Earth Fixed Frame): 3D Cartesian coordinates of right-handed system with the center of the earth as the origin and the z axis parallel to the rotation axis ・ n Frame (Navigation Frame): Origin of the moving object (user) 3D Cartesian coordinate system with x-axis as north, y-axis as east, and z-axis as gravity direction ・ B frame (Body Frame): 3D Cartesian coordinates based on sensor (Inertial Measurement Unit (IMU) 10) System • m Frame (Moving Frame): A right-handed three-dimensional Cartesian coordinate system with the moving body (user) as the origin and the traveling direction of the moving body (user) as the x-axis

1-3. System Configuration FIG. 2 is a functional block diagram showing a configuration example of the motion analysis device 2 and the display device 3. As shown in FIG. 2, the motion analysis device 2 (an example of an error estimation device) includes an inertial measurement unit (IMU) 10, a processing unit 20, a storage unit 30, a communication unit 40, and a GPS unit 50. . However, the motion analysis apparatus 2 of the present embodiment may have a configuration in which some of these components are deleted or changed, or other components are added.

  The inertial measurement unit 10 (an example of a second sensor) includes an acceleration sensor 12, an angular velocity sensor 14, and a signal processing unit 16.

  The acceleration sensor 12 detects each acceleration in the three-axis directions that intersect (ideally orthogonal) with each other, and outputs a digital signal (acceleration data) corresponding to the magnitude and direction of the detected three-axis acceleration.

  The angular velocity sensor 14 detects angular velocities in the three axial directions that intersect (ideally orthogonal) with each other, and outputs a digital signal (angular velocity data) corresponding to the magnitude and direction of the measured three axial angular velocities.

  The signal processing unit 16 receives acceleration data and angular velocity data from the acceleration sensor 12 and the angular velocity sensor 14, respectively, attaches time information to the storage unit (not shown), and stores the time information in the stored acceleration data and angular velocity data. At the same time, sensing data matching a predetermined format is generated and output to the processing unit 20.

  The acceleration sensor 12 and the angular velocity sensor 14 are ideally attached so that each of the three axes coincides with the three axes of the sensor coordinate system (b frame) with the inertial measurement unit 10 as a reference. Error occurs. Therefore, the signal processing unit 16 performs a process of converting the acceleration data and the angular velocity data into data of the sensor coordinate system (b frame) using a correction parameter calculated in advance according to the attachment angle error. Note that the processing unit 20 described later may perform the conversion process instead of the signal processing unit 16.

  Further, the signal processing unit 16 may perform temperature correction processing of the acceleration sensor 12 and the angular velocity sensor 14. Note that the processing unit 20 to be described later may perform the temperature correction processing instead of the signal processing unit 16, and the acceleration sensor 12 and the angular velocity sensor 14 may incorporate a temperature correction function.

  The acceleration sensor 12 and the angular velocity sensor 14 may output analog signals. In this case, the signal processing unit 16 performs A / D conversion on the output signal of the acceleration sensor 12 and the output signal of the angular velocity sensor 14, respectively. Then, sensing data may be generated.

  The GPS unit 50 (an example of a first sensor) receives a GPS satellite signal transmitted from a GPS satellite that is a type of positioning satellite, performs a positioning calculation using the GPS satellite signal, and performs a positioning calculation of the user in n frames. The position and speed (vector including magnitude and direction) are calculated, and GPS data with time information and positioning accuracy information added thereto is output to the processing unit 20. In addition, since the method of calculating a position and speed and the method of generating time information using GPS are publicly known, detailed description is omitted.

The processing unit 20 is configured by, for example, a CPU (Central Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), and the like, and according to various programs stored in the storage unit 30, Perform control processing. In particular, the processing unit 20 receives sensing data from the inertial measurement unit 10, receives GPS data from the GPS unit 50, and calculates a user's speed, position, posture angle, and the like using the sensing data and GPS data. In addition, the processing unit 20 performs various arithmetic processes using the calculated information to analyze the user's motion, and motion analysis information (image data, text data, sound data, etc.) including a travel route and a travel time. ) Is generated. And
The processing unit 20 transmits the generated motion analysis information to the display device 3 via the communication unit 40.

  The storage unit 30 includes various IC memories such as a ROM (Read Only Memory), a flash ROM, and a RAM (Random Access Memory), a recording medium such as a hard disk and a memory card, and the like.

  The storage unit 30 stores a motion analysis program 300 that is read by the processing unit 20 and used to execute a motion analysis process (see FIG. 10). The motion analysis program 300 includes a walking detection program 301 for executing a walking detection process (see FIG. 11) as a subroutine.

  The storage unit 30 also stores a sensing data table 310, a GPS data table 320, a calculation data table 330, motion analysis information 340, and the like.

  The sensing data table 310 is a data table that stores sensing data (detection results of the inertial measurement unit 10) received by the processing unit 20 from the inertial measurement unit 10 in time series. FIG. 3 is a diagram illustrating a configuration example of the sensing data table 310. As shown in FIG. 3, the sensing data table 310 includes the sensing data associated with the detection time 311 of the inertial measurement unit 10, the acceleration 312 detected by the acceleration sensor 12, and the angular velocity 313 detected by the angular velocity sensor 14. It is arranged in series. When the measurement starts, the processing unit 20 adds new sensing data to the sensing data table 310 every time a sampling period Δt (for example, 20 ms) elapses. Further, the processing unit 20 corrects the acceleration and the angular velocity using the acceleration bias and the angular velocity bias estimated by the error estimation using the extended Kalman filter (described later), and overwrites the corrected acceleration and the angular velocity, thereby sensing data table. 310 is updated.

  The GPS data table 320 is a data table that stores the GPS data (the detection result of the GPS unit (GPS sensor) 50) received by the processing unit 20 from the GPS unit 50 in time series. FIG. 4 is a diagram illustrating a configuration example of the GPS data table 320. As shown in FIG. 4, the GPS data table 320 includes a time 321 when the GPS unit 50 performs positioning calculation, a position 322 calculated by the positioning calculation, a speed 323 calculated by the positioning calculation, and a positioning accuracy (DOP (Dilution of Precision)). 324, GPS data associated with the signal strength 325 of the received GPS satellite signal is arranged in time series. When measurement is started, the processing unit 20 adds new GPS data and updates the GPS data table 320 every time GPS data is acquired (asynchronously with the acquisition timing of sensing data).

  The calculated data table 330 is a data table that stores the speed, position, and attitude angle calculated by the processing unit 20 using the sensing data in time series. FIG. 5 is a diagram illustrating a configuration example of the calculation data table 330. As shown in FIG. 5, the calculation data table 330 is configured by calculating data in which time 331, speed 332, position 333, and posture angle 334 calculated by the processing unit 20 are associated in time series. When measurement is started, the processing unit 20 calculates the speed, position, and attitude angle every time sensing data is acquired, that is, every time the sampling period Δt elapses, and new calculation data is stored in the calculation data table 330. Append. Further, the processing unit 20 corrects the speed, the position, and the attitude angle using the speed error, the position error, and the attitude angle error estimated by the error estimation using the extended Kalman filter, and the corrected speed, position, and attitude are corrected. The calculation data table 330 is updated by overwriting the corners.

The motion analysis information 340 is various information related to the user's motion. In the present embodiment, the processing unit 20 calculates information related to movement by walking, information related to an evaluation index of walking motion,
Includes information on walking, advice, warnings, etc.

  The communication unit 40 performs data communication with the communication unit 140 of the display device 3. The communication unit 40 receives the motion analysis information generated by the processing unit 20 and transmits it to the display device 3. The received command (measurement start / stop command, etc.) is received and sent to the processing unit 20.

  The display device 3 includes a processing unit 120, a storage unit 130, a communication unit 140, an operation unit 150, a time measuring unit 160, a display unit 170, and a sound output unit 180. However, the display device 3 of the present embodiment may have a configuration in which some of these components are deleted or changed, or other components are added.

  The processing unit 120 performs various arithmetic processes and control processes according to programs stored in the storage unit 130. For example, the processing unit 120 performs various types of processing according to the operation data received from the operation unit 150 (processing for sending a measurement start / stop command to the communication unit 140, display processing according to the operation data, sound output processing, etc.), communication A process of receiving motion analysis information from the unit 140 and sending it to the display unit 170 and the sound output unit 180, a process of generating time image data corresponding to the time information received from the time measuring unit 160 and sending it to the display unit 170, and the like.

  The storage unit 130 includes various IC memories such as a ROM that stores programs and data for the processing unit 120 to perform various processes, and a RAM that is a work area of the processing unit 120, for example.

  The communication unit 140 performs data communication with the communication unit 40 of the motion analysis device 2 and receives a command (measurement start / stop command, etc.) corresponding to the operation data from the processing unit 120 to perform motion analysis. Processing to be transmitted to the device 2, processing for receiving motion analysis information (various data such as image data, text data, and sound data) transmitted from the motion analysis device 2 and processing to send to the processing unit 120 are performed.

  The operation unit 150 obtains operation data (operation data such as measurement start / stop and display content selection) from the user, and performs processing to send the operation data to the processing unit 120. The operation unit 150 may be, for example, a touch panel display, a button, a key, a microphone, or the like.

  The timer unit 160 performs processing for generating time information such as year, month, day, hour, minute, and second. The timer unit 160 is realized by a real time clock (RTC) IC, for example.

  The display unit 170 displays the image data and text data sent from the processing unit 120 as characters, graphs, tables, animations, and other images. The display unit 170 is realized by a display such as an LCD (Liquid Crystal Display), an organic EL (Electroluminescence) display, or an EPD (Electrophoretic Display), and may be a touch panel display. Note that the functions of the operation unit 150 and the display unit 170 may be realized by a single touch panel display.

  The sound output unit 180 outputs the sound data sent from the processing unit 120 as sound such as sound or buzzer sound. The sound output unit 180 is realized by, for example, a speaker or a buzzer.

FIG. 6 is a functional block diagram illustrating a configuration example of the processing unit 20 of the motion analysis apparatus 2. In the present embodiment, the processing unit 20 executes the motion analysis program 300 stored in the storage unit 30 to thereby perform a bias removal unit 210, an integration processing unit 220, an error estimation unit 230, a walking detection unit 240, and a reference value. It functions as a calculation unit 250, a mode selection unit 260, a coordinate conversion unit 270, and a motion analysis unit 280.

Bias removal unit 210, the newly acquired acceleration included in the sensing data (three-axis acceleration) and angular velocity, respectively, the acceleration bias b _a and angular velocity bias b _omega error estimation unit 230 has estimated by subtracting the acceleration and angular velocity The process which correct | amends is performed. Since the in the initial state immediately after the start of measurement no acceleration bias b _a and angular velocity bias b _omega, as bias removal unit 210, the user's initial state is stationary, the sensing data from the inertial measurement unit To calculate the initial bias.

Integration processing unit 220, the processing bias removal unit 210 calculates the speed ^{v e} of e frame from the acceleration corrected and the angular velocity, the position ^{p e} and orientation angle (roll angle phi _BE, pitch angle theta _BE, yaw angle [psi _BE) and I do. Specifically, the integration processing unit 220 first assumes that the initial state of the user is a stationary state, sets the initial speed to zero, or calculates the initial speed from the speed included in the GPS data. The initial position is calculated from the positions included in. Further, the integration processing unit 220 calculates the initial values of the roll angle φ _be and the pitch angle θ _be by specifying the direction of the gravitational acceleration from the triaxial acceleration of the b frame corrected by the bias removal unit 210, and _converts the initial value of the GPS into the GPS data. The initial value of the yaw angle ψ _be is calculated from the included velocity, and is set as the initial posture angle of the e frame. When GPS data cannot be obtained, the initial value of the yaw angle ψ _be is set to zero, for example. Then, the integration processing unit 220 calculates an initial value of a coordinate transformation matrix (rotation matrix) C _b ^e from the b frame to the e frame represented by Expression (1) from the calculated initial attitude angle.

Then, the integration processing section 220, the integrated three-axis angular velocity bias removal unit 210 is corrected (rotation operation) and to calculate the coordinate transformation matrix C _b ^e, calculates the posture angle from the equation (2).

Further, the integration processing unit 220 uses the coordinate transformation matrix C _b ^e, the 3-axis acceleration of b frames bias removal unit 210 is corrected by converting the 3-axis acceleration of the e frame, integrated to remove the gravitational acceleration component calculate the velocity v ^e of e frame by. Further, the integration processing unit 220 calculates the position p ^{e of the} e frame by integrating the speed v ^e of the e frame.

Performing addition, the integration processing section 220, speed error .delta.v ^e the error estimator 230 estimates, using the position error .delta.p ^e and attitude angle error epsilon ^e velocity ^{v e,} also processing for correcting the position ^{p e} and orientation angle .

Furthermore, the integration processing unit 220 also calculates a coordinate conversion matrix C _b ^m from the _b frame to the m frame and a coordinate conversion matrix C _e ^m from the e frame to the m frame. These coordinate transformation matrices are used as coordinate transformation information for coordinate transformation processing of the coordinate transformation unit 270 described later.

The error estimation unit 230 estimates an error of an index representing the user's state using the speed / position, posture angle calculated by the integration processing unit 220, acceleration and angular velocity corrected by the bias removal unit 210, GPS data, and the like. In the present embodiment, the error estimation unit 230 uses the velocity, posture angle, acceleration, angular velocity, and position as indices representing the user's state, and estimates an error of these indices using an extended Kalman filter. That is, the error estimator 230, the error (velocity error) .delta.v ^e of the velocity ^{v e} of the integration processing unit 220 is calculated, an error of the posture angle integration processing unit 220 is calculated (posture angle error) epsilon ^e, acceleration bias _{b a} the angular velocity bias b _omega and integration processing unit 220 is an error of the position p ^e calculated (position error) .delta.p ^e and extended Kalman filter state variables defining the state vector X as in equation (3).

  The error estimation unit 230 predicts a state variable (an error of an index representing the user's state) included in the state vector X using a prediction formula of the extended Kalman filter. The prediction formula of the extended Kalman filter is expressed as in Equation (4). In Equation (4), the matrix Φ is a matrix that associates the previous state vector X with the current state vector X, and some of the elements are designed to change from moment to moment while reflecting the posture angle, position, and the like. The Q is a matrix representing process noise, and each element thereof is set to an appropriate value in advance. P is an error covariance matrix of state variables.

Further, the error estimation unit 230 updates (corrects) the predicted state variable (the error of the index representing the user's state) using the extended Kalman filter update formula. The extended Kalman filter update formula is expressed as shown in Formula (5). Z and H are an observation vector and an observation matrix, respectively, and the update equation (5) uses the difference between the actual observation vector Z and the vector HX predicted from the state vector X to correct the state vector X. Represents. R is an observation error covariance matrix, which may be a predetermined constant value or may be dynamically changed. K is a Kalman gain, and the smaller R is, the larger K is. From equation (5), the larger the K (the smaller R), the larger the amount of correction of the state vector X, and the smaller P.

  Examples of error estimation methods (state vector X estimation methods) include the following.

Error estimation method by correction based on attitude angle error:
FIG. 7 is an overhead view of the movement of the user when the user wearing the motion analysis device 2 on the right waist performs a walking motion (straight forward). FIG. 8 is a diagram illustrating an example of the yaw angle (azimuth angle) calculated from the detection result of the inertial measurement unit 10 when the user performs a walking motion (straight forward), where the horizontal axis represents time and the vertical axis represents yaw. It is an angle (azimuth).

As the user walks, the posture of the inertial measurement unit 10 with respect to the user changes at any time. When the user steps on the right foot, as shown in (2) and (4) in FIG. 7, the inertial measurement unit 10 is inclined to the left with respect to the traveling direction (the x axis of the m frame). On the other hand, when the user steps on the left foot, the inertial measurement unit 10 tilts to the right with respect to the traveling direction (the x axis of the m frame) as shown in (1) and (3) in FIG. Become posture. That is, the posture of the inertial measurement unit 10 periodically changes every two steps, one step left and right, with the user's walking motion. In FIG. 8, for example, the yaw angle is maximized when the right foot is stepped on (◯ in FIG. 8), and the yaw angle is minimized when the left foot is stepped on (● in FIG. 8). Therefore, the error can be estimated assuming that the previous posture angle (two steps before) and the current posture angle are equal and the previous posture angle is a true posture. In this method, the observation vector Z and the observation matrix H are as shown in Equation (6). In Equation (6), O _3,3 is a 3-by-3 zero matrix, I ₃ is a 3-by-3 unit matrix, and O _3,9 is a 3-by-9 zero matrix. Ψ in equation (6) is calculated by equation (7).

In Equation (7), C _b ^e (+) is the current posture angle, and C _b ^e (−) is the previous posture angle. The observation vector Z in Equation (6) is the difference between the previous posture angle and the current posture angle. In this method, the state based on the difference between the posture angle error ε ^e and the observed value is obtained by the update equation (5). Correct the vector X and estimate the error.

Error estimation method by correction based on angular velocity bias:
This is a method of estimating an error on the assumption that the previous posture angle (two steps before) and the current posture angle are equal, but the previous posture angle does not have to be a true posture. In this method, the observation vector Z and the observation matrix H are as shown in Equation (8). In equation (8), O _3,9 is a 3-by-9 zero matrix, I ₃ is a 3-by-3 unit matrix, and O _3,3 is a 3-by-3 zero matrix.

In Equation (8), C _b ^e (+) is the current posture angle, and C _b ^e (−) is the previous posture angle. Also, τ _{− +} is the time until the previous posture angle changes to the current posture angle. The observation vector Z in the equation (8) is an angular velocity bias calculated from the previous posture angle and the current posture angle. In this method, based on the difference between the angular velocity bias _bω and the observed value by the update equation (5). The state vector X is corrected to estimate the error.

Error estimation method by correction based on azimuth error:
The previous yaw angle (azimuth angle) and the current yaw angle (azimuth angle) are the same, and the previous yaw angle (azimuth angle) is the true yaw angle (azimuth angle). This is an estimation method. In this method, the observed vector Z is the difference between the previous yaw angle calculated by the integration processing unit 220 and the current yaw angle, and is based on the difference between the azimuth error ε _z ^e and the observed value by the update equation (5). The state vector X is corrected to estimate the error.

Error estimation method by correction based on stop:
This is a method of estimating the error on the assumption that the speed is zero at the time of stopping. In this method, the observation vector Z is the difference between the speed v ^e and zero integration processing unit 220 is calculated, the update equation (5), corrects the state vector X on the basis of the speed error .delta.v ^e, estimating the error To do.

Error estimation method by correction based on stillness:
This is a method of estimating the error on the assumption that the speed is zero and the posture change is zero at the time of stationary. In this method, observation vector Z is the difference between the previous attitude angle and the current posture angle error and the integration processing unit 220 calculates the velocity v ^e of the integration processing unit 220 is calculated, the update equation (5), correcting the state vector X on the basis of the speed error .delta.v ^e and attitude angle error epsilon ^e, estimating the error.

Error estimation method by correction based on GPS observations:
It is assumed that the speed v ^e , the position p ^e or the yaw angle ψ _be calculated by the integration processing unit 220 and the speed, position or azimuth calculated from the GPS data (speed, position and azimuth converted to e frame) are equal. This is a method for estimating the error. In this method, the observation vector Z is the difference between the speed, position or yaw angle calculated by the integration processing unit 220 and the speed, position speed or azimuth calculated from the GPS data. .delta.v ^e, corrects the state vector X on the basis of the difference between the observed value and the position error .delta.p ^e or azimuth error epsilon _z ^e, estimating the error. Among these, in the “error estimation method by correction based on the azimuth angle of GPS” using the azimuth angle calculated from the GPS data as a reference, the observation vector Z and the observation matrix H are expressed by Equation (9). In Expression (9), ψ _INS is an azimuth angle calculated from the yaw angle ψ _be calculated by the integration processing unit 220, and ψ _ref is a reference azimuth angle. In Equation (9), O _1,3 is a zero matrix with 1 row and 3 columns, and O _1,9 is a zero matrix with 1 row and 9 columns. Each partial derivative of equation (9) is calculated by equation (10). _{Further, n 1, n 2, n} 3, d 1, d 2, d 3 of the formula (10) is calculated by the equation (11).

Among these, “Error estimation method based on correction based on attitude angle error”, “Error estimation method based on correction based on azimuth angle error”, “Error estimation method based on correction based on angular velocity bias” (hereinafter referred to as “error using attitude angle”) The term “estimation method” is an advantage that external information such as GPS data is unnecessary and can be applied during walking. In these methods, since it is a condition that the posture angle (azimuth angle) of the previous time (two steps before) and the current posture angle (azimuth angle) are equal, the timing of almost the same posture angle every two steps. It is necessary to estimate the error.

  Returning to FIG. 6, the walking detection unit 240 detects the user's walking cycle (walking timing) using the detection result of the inertial measurement unit 10 (specifically, sensing data corrected by the bias removing unit 210). I do. As described with reference to FIGS. 7 and 8, since the user's posture changes periodically (every two steps (one step on each of the left and right)) when the user walks, the acceleration detected by the inertial measurement unit 10 also periodically Change. FIG. 9 is a diagram illustrating an example of the triaxial acceleration detected by the inertial measurement unit 10 when the user walks. In FIG. 9, the horizontal axis represents time, and the vertical axis represents the acceleration value. As shown in FIG. 9, it can be seen that the triaxial acceleration changes periodically, and in particular, the z axis (axis in the direction of gravity) acceleration changes regularly with periodicity. This z-axis acceleration reflects the acceleration of the user's vertical movement, and the period from when the z-axis acceleration reaches a maximum value greater than a predetermined threshold to the next maximum value greater than the threshold is one step. It corresponds to. Then, one step when the right foot is stepped on and one step when the left foot is stepped on are alternately repeated.

  Therefore, in the present embodiment, the walking detection unit 240 is performed once every two times when the z-axis acceleration (corresponding to the acceleration of the user's vertical movement) detected by the inertial measurement unit 10 becomes a maximum value equal to or greater than a predetermined threshold. Detecting the walking cycle. However, since the z-axis acceleration detected by the inertial measurement unit 10 actually includes a high-frequency noise component, the gait detection unit 240 passes the z-axis acceleration through a low-pass filter to remove the noise. The walking cycle is detected using acceleration.

  The reference value calculation unit 250 generates a reference value (reference) for error estimation using GPS data in synchronization with the timing at which GPS data (detection result of the GPS unit 50) is obtained (updated). For example, the reference value calculation unit 250 calculates the azimuth angle of the user's traveling direction as the reference value. In this case, for example, the reference value calculation unit 250 may calculate the azimuth angle of the traveling direction from the direction of the speed included in the GPS data, or the azimuth angle of the traveling direction from the two positions included in the two GPS data. May be calculated, or when the GPS data includes azimuth information, the azimuth may be the azimuth of the traveling direction.

  The mode selection unit 260 selects either the first mode or the second mode as the error estimation mode of the error estimation unit 230 based on the GPS data (the detection result of the GPS unit 50). In this embodiment, the mode selection unit 260 is the timing when the GPS data is updated after the previous walking cycle is detected at the timing when the walking detection unit 240 detects the walking cycle (cycle for two steps). 1 mode is selected, and if not updated, the second mode is selected. For example, the mode selection unit 260 stores the time when the walking cycle is detected, compares the time 321 of the latest GPS data included in the GPS data table 320 with the time when the walking cycle is detected, and sets the GPS data. It can be determined whether or not has been updated.

When the mode selection unit 260 selects the first mode, the error estimation unit 230 uses the state vector X predicted by the prediction formula (4) as a second reference based on sensing data (detection result of the inertial measurement unit 10). The first reference value based on the GPS data (detection result of the GPS unit 50) and the first state variable included in the state vector X without using the value (of the error of the index indicating the user state) And an update equation (5). In addition, when the mode selecting unit 260 selects the second mode, the error estimating unit 230 uses the state vector X predicted by the prediction formula (4) as the first based on the GPS data (the detection result of the GPS unit 50). Without using the reference value, the second reference value based on the sensing data (detection result of the inertial measurement unit 10) and the first state variable included in the state vector X are used to correct by the update formula (5). In short, in the first mode, the error estimation unit 230 does not use the second reference value based on the calculation result of the integration processing unit 220 but uses the first reference value calculated by the reference value calculation unit 250 from the GPS data. A vector Z is calculated, and an observation matrix H is set such that HX becomes the value of the first state variable. Further, in the second mode, the error estimation unit 230 does not use the first reference value calculated from the GPS data by the reference value calculation unit 250, but uses the second reference value based on the calculation result of the integration processing unit 220. A vector Z is calculated, and an observation matrix H is set such that HX becomes the value of the first state variable.

For example, in the first mode, the error estimation unit 230 uses the “error estimation method based on correction based on the GPS azimuth angle” to calculate the azimuth angle calculated by the reference value calculation unit 250 as the reference value ψ _ref (first reference value). The observation vector Z is calculated by the equation (9) as the equation (9), the observation matrix H of the equation (9) is set, the azimuth error (corresponding to the first state variable) is calculated as HX, and the update equation ( The state vector X is corrected by 5). In addition, for example, in the second mode, the error estimation unit 230 uses the “error estimation method by correction based on the attitude angle error” as the reference value C _b ^e (the previous coordinate transformation matrix calculated by the integration processing unit 220. -) (Corresponding to the second reference value), the observation vector Z is calculated by the equations (6) and (7), the observation matrix H of the equation (6) is set, and a three-dimensional attitude angle error ( Direction error is included as an element), and the state vector X is corrected by the update equation (5). In both the correction of the state vector X in the first mode and the correction of the state vector X in the second mode, an azimuth error (corresponding to the first state variable) is calculated as Z and HX. In short, the error estimation unit 230 uses two different error estimation methods (hereinafter referred to as “two overlapping corrections” for correcting the state vector X using the difference (Z−HX) of the same state variables included in the state vector X. When the error estimation method is used in combination, only one of the first mode and the second mode is exclusively selected. As described above, by exclusively selecting one of the two error estimation methods for performing overlapping correction, double estimation is not performed, and a decrease in error estimation accuracy can be suppressed. .

  Further, when the mode selection unit 260 selects the first mode, the error estimation unit further uses the state vector X predicted by the prediction formula (4) based on the sensing data (the detection result of the inertial measurement unit 10). Using the third reference value and the second state variable of the state variables included in the state vector X (an example of the second error of the error of the index representing the user's state), correction is performed by the update equation (5) To do. In short, in the first mode, the error estimation unit 230 calculates the observation vector Z using the first reference value calculated from the GPS data by the reference value calculation unit 250 and the third reference value based on the calculation result of the integration processing unit 220. The observation matrix H is calculated so that the elements of HX are the values of the first state variable and the second state variable.

For example, in the first mode, the error estimation unit 230 uses the “error estimation method based on correction based on the GPS azimuth angle” to calculate the azimuth angle calculated by the reference value calculation unit 250 as the reference value ψ _ref (first reference value). And the previous coordinate transformation matrix calculated by the integration processing unit 220 using the “error estimation method by correction based on the angular velocity bias” is referred to as a reference value C _b ^e (−) (the third reference value). As a result, an observation vector obtained by combining Z in Expression (9) and Z in Expression (8) is calculated. Further, the error estimation unit 230 sets an observation matrix obtained by combining the observation matrix H of Expression (9) and the observation matrix H of Expression (8), and the value of the direction error (corresponding to the first state variable) and the angular velocity. HX having a bias (corresponding to the second state variable) as an element is calculated, and the state vector X is corrected by the update equation (5). In short, in the first mode, the error estimation unit 230 corrects the state vector X using the difference (Z−HX) of different state variables included in the state vector X (hereinafter, “not overlapping”). "Two error estimation methods for correction" are applied simultaneously. As described above, even if two error estimation methods for performing non-overlapping correction are applied at the same time, double estimation is not performed, and the error estimation accuracy can be improved.

The coordinate conversion unit 270 uses the b-frame to m-frame coordinate conversion information (coordinate conversion matrix C _b ^m ) calculated by the integration processing unit 220 to calculate the acceleration and angular velocity of the b frame corrected by the bias removal unit 210, respectively. A coordinate conversion process is performed to convert the acceleration and angular velocity of m frames. In addition, the coordinate conversion unit 270 uses the coordinate conversion information (coordinate conversion matrix C _e ^m ) from the e frame to the m frame calculated by the integration processing unit 220, and the speed and position of the e frame calculated by the integration processing unit 220. And a coordinate conversion process for converting the posture angle into the velocity, position, and posture angle of m frames, respectively.

  The motion analysis unit 280 performs various calculations using the m-frame acceleration, angular velocity, speed, position, and posture angle after the coordinate conversion unit 270 performs coordinate conversion, analyzes the user's motion, and generates motion analysis information 340. Generate the process. In the present embodiment, the motion analysis unit 280 includes information on movement such as a movement route, movement speed, and movement time, a degree of forward tilt, a difference between left and right movements, propulsion efficiency, energy consumption, and energy efficiency. Information on evaluation index of walking movement such as, information on advice and guidance for better walking, warning information indicating that posture is bad (information for causing display device 3 to output warning display, warning sound, etc.), etc. The motion analysis information 340 including is generated.

  The processing unit 20 transmits the motion analysis information 340 to the display device 3, and the motion analysis information 340 is displayed on the display unit 170 of the display device 3 as text, an image, a figure, or the like, Or buzzer sound. Basically, by displaying the motion analysis information 340 on the display unit 170, the user can check the display unit 170 when he / she wants to know the motion analysis information, and information (warning information) that he / she wants to call attention to the user. Etc.) at least as a sound, the user need not always walk while watching the display unit 170.

1-4. Processing Procedure FIG. 10 is a flowchart showing an example of a procedure of motion analysis processing by the processing unit 20 (an example of a motion analysis method). The processing unit 20 executes the motion analysis program 300 by executing the motion analysis program 300 stored in the storage unit 30, thereby executing the motion analysis process according to the procedure of the flowchart of FIG. 10.

  As shown in FIG. 10, when the processing unit 20 receives a measurement start command (Y in S1), first, the sensing data measured by the inertial measurement unit 10 and GPS Using the data, the initial posture, initial position, and initial bias are calculated (S2).

  Next, the processing unit 20 acquires sensing data from the inertial measurement unit 10, and adds the acquired sensing data to the sensing data table 310 (S3).

Next, the sensing processing section 20, (after estimating the acceleration bias b _a and angular velocity bias b _omega at S15, by using the acceleration bias b _a and angular velocity bias b _omega) by using the initial bias was acquired in S3 The correction is performed by removing the bias from the acceleration and angular velocity included in the data, and the sensing data table 310 is updated with the corrected acceleration and angular velocity (S4).

  Next, the processing unit 20 integrates the sensing data corrected in S4 to calculate the speed, position, and attitude angle, and adds calculation data including the calculated speed, position, and attitude angle to the calculation data table 330 (S5). ).

  Next, when the GPS data is updated (Y in S6), the processing unit 20 calculates a reference value (for example, an azimuth reference value) from the GPS data (S7), and turns on the GPS data update flag (S7). S8). On the other hand, when the GPS data is not updated (N in S6), the processing unit 20 does not perform the process of S7 and the process of S8.

  Next, the processing unit 20 performs a walking detection process (S9). An example of the procedure of the walking detection process will be described later.

Next, when the walking cycle is detected by the walking detection process (S9) (Y in S10), the processing unit 20 selects the first mode if the GPS data update flag is on (Y in S11) (S12). ), Error estimation processing is performed in the first mode (S13), and the GPS data update flag is turned off (S14). Processing unit 20, in S13, to apply the Kalman filter used without using the second reference value calculated from the sensing data, the first reference value calculated in S7 and the first state variable, speed error .delta.v ^e, attitude angle error epsilon ^e, acceleration bias _{b a,} the angular bias b _omega and position error .delta.p ^e estimates. The processing unit 20, in S13, to calculate the third reference value based on the sensing data, to apply the Kalman filter using the third reference value and a second state variable, speed error .delta.v ^e, attitude angle error ε ^e , acceleration bias b _a , angular velocity bias b _ω and position error δp ^e are estimated.

If the GPS data update flag is off (N in S11), the processing unit 20 selects the second mode (S15), and performs error estimation processing in the second mode (S16). In S16, the processing unit 20 calculates the second reference value based on the sensing data, and applies the Kalman filter using the second reference value and the first state variable without using the first reference value based on the GPS data. and, speed error .delta.v ^e, attitude angle error epsilon ^e, acceleration bias _{b a,} the angular bias b _omega and position error .delta.p ^e estimates.

Then, the processing unit 20 uses the speed error .delta.v ^e, attitude angle error epsilon ^e and position error .delta.p ^e estimated in S13 or S16, the speed, position and orientation angle was corrected, respectively, in the corrected speed, position Then, the calculation data table 330 is updated with the attitude angle (S17).

  Moreover, the process part 20 does not perform the process of S11-S17, when a walk period is not detected (N of S10).

  Next, the processing unit 20 detects the sensing data (b frame acceleration and angular velocity) stored in the sensing data table 310 and the calculated data (e frame velocity, position, and attitude angle) stored in the calculation data table 330. Is converted into m frame acceleration, angular velocity, velocity, position, and posture angle (S18). The processing unit 20 stores the acceleration, angular velocity, speed, position, and posture angle of the m frame in the storage unit 30 in time series.

  Next, the processing unit 20 analyzes the user's motion in real time using the m-frame acceleration, angular velocity, velocity, position, and posture angle after the coordinate conversion in S18, and generates motion analysis information (S19). .

  Next, the processing unit 20 transmits the motion analysis information generated in S19 to the display device 3 (S20). The motion analysis information transmitted to the display device 3 is fed back in real time while the user is walking. In this specification, “real time” means that processing is started at the timing when information to be processed is acquired. Therefore, it includes the case where there is a certain time difference between the acquisition of information and the completion of processing.

Then, the processing unit 20 receives a measurement stop command (N in S21 and N in S22) every time the sampling period Δt elapses after acquiring the previous sensing data (Y in S21), and after S3 Repeat the process. When a measurement stop command is received (Y in S22), the user performed using the m-frame acceleration, angular velocity, velocity, position, posture angle, and the analysis result of S19, which are coordinate-converted in S18 and stored in time series. The motion is analyzed, and motion analysis information is generated (S23). In S23, the processing unit 20 may perform the motion analysis process immediately upon receiving the measurement stop command, or may perform the motion analysis processing when the motion analysis command by the user's operation is received. Further, the processing unit 20 may transmit the motion analysis information generated in S23 to the display device 3, may transmit the information to a device such as a personal computer or a smartphone, or may record the information on a memory card.

  In FIG. 10, if the processing unit 20 does not receive the measurement start command (N in S1), the processing of S1 to S23 is not performed, but the m-frame acceleration, angular velocity, speed, The process of S23 may be performed using the position and orientation angle and the analysis result of S19.

  FIG. 11 is a flowchart showing an example of the procedure of the walking detection process (the process of S9 in FIG. 10). The processing unit 20 (walking detection unit 240) executes the walking detection process according to the procedure of the flowchart of FIG. 11 by executing the walking detection program 301 stored in the storage unit 30.

  As shown in FIG. 11, the processing unit 20 performs low-pass filter processing on the z-axis acceleration included in the acceleration corrected in S4 of FIG. 10 (S100), and removes noise.

  Next, when the z-axis acceleration subjected to low-pass processing in S100 is equal to or greater than the threshold value and the maximum value (Y in S110), the processing unit 20 walks at this timing if the walking detection valid flag is on (Y in S120). The period is detected (S130). And the process part 20 turns off a walk detection effective flag (S140), and complete | finishes a walk detection process.

  On the other hand, if the z-axis acceleration is equal to or greater than the threshold value and the maximum value (Y in S110) and the walking detection valid flag is off (N in S120), the walking detection valid flag is turned on without detecting the walking cycle. (S150), the walking detection process is terminated. If the z-axis acceleration is not less than the threshold value or the maximum value (N in S110), the processing unit 20 ends the walking process without performing the processes after S120.

1-5. Effect According to the present embodiment, since either one of two error estimation methods for performing overlapping correction is exclusively selected and used without overlapping, an error of an index representing the user state is accurately estimated. be able to.

  Further, according to the present embodiment, when GPS data is obtained most recently at the detection timing of the walking cycle, error estimation can be performed in the first mode with relatively high reliability, and the GPS Even when data is not obtained, error estimation can be performed in the second mode.

  In addition, according to the present embodiment, in the first mode, two error estimation methods that perform non-overlapping corrections are applied simultaneously, so that double estimation is not performed, and error estimation accuracy with the first accuracy is achieved. Can be improved.

  Further, according to the present embodiment, information such as the user's speed, position, posture angle, and the like can be accurately corrected using the accurately estimated error. Furthermore, according to the present embodiment, it is possible to analyze the user's walking motion with high accuracy using the information such as the user's speed, position, posture, etc. corrected with high accuracy.

2. The present invention is not limited to this embodiment, and various modifications can be made within the scope of the present invention. Hereinafter, modified examples will be described. In addition, about the structure same as the said embodiment, the same code | symbol is attached | subjected and description for the second time is abbreviate | omitted.

2-1. Sensor In the above embodiment, the acceleration sensor 12 and the angular velocity sensor 14 are built in the motion analysis apparatus 2 integrated as the inertial measurement unit 10, but the acceleration sensor 12 and the angular velocity sensor 14 are not integrated. Good. Alternatively, the acceleration sensor 12 and the angular velocity sensor 14 may be directly attached to the user without being built in the motion analysis apparatus 2. In either case, for example, any one of the sensor coordinate systems may be used as the b frame of the above embodiment, the other sensor coordinate system may be converted into the b frame, and the above embodiment may be applied.

  Further, in the above-described embodiment, the site where the sensor (the motion analysis apparatus 2 (IMU 10)) is attached to the user has been described as the waist, but it may be attached to a site other than the waist. A suitable wearing part is a user's trunk (parts other than limbs). However, it is not limited to the trunk, and may be worn on the user's head or feet other than the arms.

2-2. Walking detection In the above-described embodiment, the walking detection unit 240 detects the walking cycle at a timing when the acceleration of the user's vertical movement (z-axis acceleration) becomes a maximum value when the threshold value is greater than or equal to the threshold value. The walking cycle may be detected at a timing at which vertical acceleration (z-axis acceleration) changes from positive to negative (zero crossing) (or timing at which zero crossing from negative to positive). Alternatively, the walking detection unit 240 calculates the vertical movement speed (z-axis speed) by integrating the vertical movement acceleration (z-axis acceleration), and uses the calculated vertical movement speed (z-axis speed). May be detected. In this case, for example, the walking detector 240 may detect the walking cycle at a timing when the speed crosses the threshold value near the median value between the maximum value and the minimum value by increasing the value or by decreasing the value. Further, for example, the walking detection unit 240 may calculate the combined acceleration of the x-axis, the y-axis, and the z-axis, and detect the walking cycle using the calculated combined acceleration. In this case, for example, the walking detection unit 240 may detect the walking cycle at a timing when the resultant acceleration crosses the threshold value near the median value between the maximum value and the minimum value by increasing the value or by decreasing the value. .

2-3. Mode Selection In the above embodiment, the mode selection unit 260 selects the first mode when the GPS data is updated, and selects the second mode when the GPS data is not updated. When the positioning accuracy of the GPS data is greater than or equal to the reference value (when the DOP value is less than or equal to the reference value), the first mode is selected, and when the positioning accuracy is less than the reference value (the DOP value is The second mode may be selected when it is larger than the reference value.

2-4. Reference Value Calculation In the above-described embodiment, the reference value calculation unit 250 calculates a reference value using a signal from a GPS satellite, but a global navigation satellite system (GNSS) other than GPS is used. The reference speed may be calculated using signals from positioning satellites or positioning satellites other than GNSS. Alternatively, the reference value calculation unit 250 may calculate the reference value of the azimuth using the detection signal of the geomagnetic sensor. For example, one or more satellite positioning systems such as WAAS (Wide Area Augmentation System), QZSS (Quasi Zenith Satellite System), GLONASS (GLObal NAvigation Satellite System), GALILEO, and BeiDou (BeiDou Navigation Satellite System) are used. May be. Further, an indoor positioning system (IMES: Indoor Messaging System) or the like may be used.

2-5. Error Estimation In the above-described embodiment, the error estimation unit 230 uses the velocity, posture angle, acceleration, angular velocity, and position as indices representing the user's state, and estimates an error of these indices using an extended Kalman filter. The error may be estimated using a part of the velocity, posture angle, acceleration, angular velocity, and position as an index representing the user's state. Alternatively, the error estimation unit 230 may estimate the error using an index (eg, movement distance) other than the speed, the posture angle, the acceleration, the angular velocity, and the position as an index representing the user state.

  Further, in the above embodiment, the error estimation unit 230 corrects the state vector X using the reference value of the azimuth angle and the azimuth angle error in the first mode and the second mode, and performs two corrections that perform overlapping correction. Although the error estimation method is exclusively selected, the present invention is not limited to this. For example, two error estimation methods for correcting the state vector X using the distance reference value and the distance error and performing overlapping correction are excluded. May be selected. Specifically, a distance error is defined as a state variable of the state vector X. In the first mode, the error estimation unit 230 uses a distance reference value based on GPS data as a first reference value, and a distance error as a first error. The state vector X is corrected using the first reference value and the first state variable as the state variables. Further, in the second mode, the error estimation unit 230 refers to the distance reference value based on the sensing data (for example, estimating the step length from the acceleration and calculating the distance reference value by multiplying the step length and the number of steps). The state vector X is corrected using the first reference value and the first state variable with the error of the value and distance as the first state variable.

  In the above-described embodiment, the extended Kalman filter is used for error estimation by the error estimation unit 230, but other estimation means such as a particle filter or an H∞ (H infinity) filter may be used.

2-6. Others In the above embodiment, the integration processing unit 220 calculates the speed, position, and posture angle of the e frame, and the coordinate conversion unit 270 converts the coordinate to the speed, position, and posture angle of the m frame. The processing unit 220 may calculate the speed, position, and posture angle of m frames. In this case, the motion analysis unit 280 may perform the motion analysis process using the m-frame speed, position, and posture angle calculated by the integration processing unit 220. Therefore, the coordinate of the speed, position, and posture angle by the coordinate conversion unit 270 may be used. No conversion is required. Further, the error estimation unit 230 may perform error estimation using an extended Kalman filter using the speed, position, and attitude angle of m frames.

  In the above-described embodiment, the processing unit 20 generates motion analysis information such as image data, sound data, and text data. However, the present invention is not limited to this, and for example, the processing unit 20 includes propulsion efficiency and energy consumption. The processing unit 120 of the display device 3 that has received the calculation result may generate image data, audio data, text data (such as advice) according to the calculation result.

  Moreover, in said embodiment, although the process part 20 receives the measurement stop command, it performs the process (S23 of FIG. 10) which analyzes the exercise | movement which the user performed and produces | generates exercise | movement analysis information. This motion analysis process (post-processing) may not be performed by the processing unit 20. For example, the processing unit 20 may transmit various types of information stored in the storage unit 30 to devices such as a personal computer, a smartphone, and a network server, and these devices may perform motion analysis processing (post-processing).

  In the above embodiment, the display device 3 outputs the motion analysis information from the display unit 170 and the sound output unit 180. However, the display device 3 is not limited thereto. Various information may be output by vibrating the mechanism in various patterns.

In the above embodiment, the GPS unit 50 is provided in the motion analysis device 2, but may be provided in the display device 3. In this case, the processing unit 120 of the display device 3 receives GPS data from the GPS unit 50 and transmits it to the motion analysis device 2 via the communication unit 140, and the processing unit 20 of the motion analysis device 2 performs GPS via the communication unit 40. Data may be received and the received GPS data may be added to the GPS data table 320.

  Further, in the above embodiment, the motion analysis device 2 and the display device 3 are separate, but a motion analysis device in which the motion analysis device 2 and the display device 3 are integrated may be used.

  In the above embodiment, the motion analysis device 2 is attached to the user. However, the present invention is not limited to this, and an inertial measurement unit (inertia sensor) or a GPS unit is attached to the user's torso, etc. The sensor) and the GPS unit may transmit the detection result to a portable information device such as a smartphone or a stationary information device such as a personal computer, and analyze the user's movement using the detection result received by these devices. . Alternatively, an inertial measurement unit (inertial sensor) or GPS unit mounted on the user's body or the like records the detection result on a recording medium such as a memory card, and the information medium such as a smartphone or a personal computer records the recording medium. The motion analysis processing may be performed by reading out the detection result from.

  Moreover, in said embodiment, although human walk was made into object, this invention is applicable not only to this but the walk of moving bodies, such as an animal and a walking robot, similarly. In addition, the present invention is not limited to walking, but includes various activities such as mountain climbing, trail running, skiing (including cross-country and ski jumping), snowboarding, swimming, cycling, skating, golf, tennis, baseball, rehabilitation, and the like. Can be applied to.

  Each embodiment and each modification mentioned above are examples, and are not limited to these. For example, it is possible to appropriately combine each embodiment and each modification.

  The present invention includes configurations that are substantially the same as the configurations described in the embodiments (for example, configurations that have the same functions, methods, and results, or configurations that have the same objects and effects). In addition, the invention includes a configuration in which a non-essential part of the configuration described in the embodiment is replaced. In addition, the present invention includes a configuration that exhibits the same operational effects as the configuration described in the embodiment or a configuration that can achieve the same object. Further, the invention includes a configuration in which a known technique is added to the configuration described in the embodiment.

DESCRIPTION OF SYMBOLS 1 Motion analysis system, 2 Motion analysis apparatus, 3 Display apparatus, 10 Inertial measurement unit (IMU), 12 Acceleration sensor, 14 Angular velocity sensor, 16 Signal processing part, 20 Processing part, 30 Storage part, 40 Communication part, 50 GPS unit , 120 processing unit, 130 storage unit, 140 communication unit, 150 operation unit, 160 timing unit, 170 display unit, 180 sound output unit, 210 bias removal unit, 220 integration processing unit, 230 error estimation unit, 240 walking detection unit, 250 reference value calculation unit, 260 mode selection unit, 270 coordinate conversion unit, 280 motion analysis unit

Claims (9)

Selecting one of the first mode and the second mode based on the detection result of the first sensor;
Estimating an error of an index representing a state of a moving object,
Estimating the error is
Predicting the error;
When the first mode is selected, without using the second reference value based on the detection result of the second sensor, the first reference value based on the detection result of the first sensor and the first error of the errors, Correcting the predicted error using
Correcting the predicted error using the second reference value and the first error without using the first reference value when the second mode is selected, including error estimation Method.   The error estimation method according to claim 1, wherein the second sensor includes at least one of an acceleration sensor and an angular velocity sensor.   The error estimation method according to claim 1, wherein the first sensor is a sensor that receives a signal from a positioning satellite or a geomagnetic sensor.   The first mode is selected when the detection result of the first sensor is obtained, and the second mode is selected when the detection result of the first sensor is not obtained. The error estimation method according to any one of the above.   The first mode is selected when the accuracy of the detection result of the first sensor is greater than or equal to a reference value, and the second mode is selected when the accuracy of the detection result of the first sensor is less than the reference value. The error estimation method according to any one of claims 1 to 3.   Further, when the first mode is selected, the prediction is performed using a third reference value based on a detection result of the second sensor and a second error different from the first error among the errors. The error estimation method according to claim 1, comprising correcting the error. Estimating the error using the error estimation method according to any one of claims 1 to 6,
Correcting the indicator using the estimated error;
Analyzing the motion of the moving body using the corrected index. A mode selection unit that selects one of the first mode and the second mode based on the detection result of the first sensor;
An error estimator for estimating an error of an index representing the state of the moving object,
The error estimator is
When the error is predicted and the first mode is selected, the first reference value based on the detection result of the first sensor and the second reference value based on the detection result of the second sensor are not used. The predicted error is corrected using the first error of the errors, and when the second mode is selected, the first reference value is not used but the second reference value and the first error are selected. An error estimation apparatus that corrects the predicted error using one error. Selecting one of the first mode and the second mode based on the detection result of the first sensor;
Estimating the error of the index representing the state of the moving object, and causing the computer to execute,
Estimating the error is
Predicting the error;
When the first mode is selected, without using the second reference value based on the detection result of the second sensor, the first reference value based on the detection result of the first sensor and the first error of the errors, Correcting the predicted error using
And correcting the predicted error using the second reference value and the first error without using the first reference value when the second mode is selected.